Cost Effectiveness of Positron Emission Tomography for the Management of Potentially Operable Non‐Small Cell Lung Cancer in Quebec
Bibliographic record
Abstract
BACKGROUND: The potential benefits of positron emission tomography (PET) scanning stem from the fact that it can reduce the number of diagnostic examinations; particularly, the number of unnecessary thoracic surgeries. OBJECTIVE: To evaluate the economic impact and cost-effectiveness of PET scanning in the management of potentially operable non-small cell lung cancer in Quebec. METHODS: A decision tree was developed. Two strategies were compared: chest computed tomography (CT) alone or CT and whole-body PET. The various paths of each strategy were dependent on the numerous variables that were determined from a literature review. The costs and life expectancy were determined for each strategy under consideration. Life expectancy was calculated using the declining exponential approximation of life expectancy. Costs were obtained from the Quebec diagnosis-related group database and Quebec physician fee schedules. RESULTS: The mean cost of the CT strategy was US dollars 8,455 per patient compared with US dollars 9,723 for the PET strategy, for a cost differential of US dollars 1,268. The PET strategy extended life expectancy by slightly more than three months (0.27 years) compared with the survival of the CT strategy. The incremental cost-effectiveness ratio was US dollars 4,689. Considering the number of new cases and the prevalence of mediastinal metastases, the budget impact would be US dollars 8,613,693. CONCLUSION: The use of PET to detect local and distant metastases in non-small cell lung cancer is an intervention that would require an acceptable investment for each life-year gained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".